Titlebook: Machine Learning and Data Mining in Pattern Recognition; 14th International C Petra Perner Conference proceedings 2018 Springer Internation

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Machine Learning and Data Mining in Pattern Recognition14th International C

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Document Clustering Using Local and Universal Knowledge,ed to combine the similarities of each pair of documents derived from the local and the universal knowledge. In the second method, the local and the universal knowledge are combined, per document, by concatenating each document’s feature vector derived from the local knowledge to the document featur

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,Adaptive Adjacency Kanerva Coding for?Memory-Constrained Reinforcement Learning,ly-adjusting generalization to assigned memory resources to provide high-quality approximation. The memory size and memory allocation no longer need to be manually assigned before and during RL. Based on our results, this approach performs well both in terms of approximation quality and memory usage